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    "# GaussNewtonOptimizer\n",
    "\n",
    "## Overview\n",
    "\n",
    "The `GaussNewtonOptimizer` class in GTSAM is designed to optimize nonlinear factor graphs using the Gauss-Newton algorithm. This class is particularly suited for problems where the cost function can be approximated well by a quadratic function near the minimum. The Gauss-Newton method is an iterative optimization technique that updates the solution by linearizing the nonlinear system at each iteration.\n",
    "\n",
    "The Gauss-Newton algorithm is based on the idea of linearizing the nonlinear residuals $r(x)$ around the current estimate $x_k$. The update step is derived from solving the normal equations:\n",
    "\n",
    "$$ J(x_k)^T J(x_k) \\Delta x = -J(x_k)^T r(x_k) $$\n",
    "\n",
    "where $J(x_k)$ is the Jacobian of the residuals with respect to the variables. The solution $\\Delta x$ is used to update the estimate:\n",
    "\n",
    "$$ x_{k+1} = x_k + \\Delta x $$\n",
    "\n",
    "This process is repeated iteratively until convergence.\n",
    "\n",
    "Key features:\n",
    "\n",
    "- **Iterative Optimization**: The optimizer refines the solution iteratively by linearizing the nonlinear system around the current estimate.\n",
    "- **Convergence Control**: It provides mechanisms to control the convergence through parameters such as maximum iterations and relative error tolerance.\n",
    "- **Integration with GTSAM**: Seamlessly integrates with GTSAM's factor graph framework, allowing it to be used with various types of factors and variables.\n",
    "\n",
    "## Key Methods\n",
    "\n",
    "Please see the base class [NonlinearOptimizer.ipynb](NonlinearOptimizer.ipynb).\n",
    "\n",
    "## Parameters\n",
    "\n",
    "The Gauss-Newton optimizer uses the standard optimization parameters inherited from `NonlinearOptimizerParams`, which include:\n",
    "\n",
    "- Maximum iterations\n",
    "- Relative and absolute error thresholds\n",
    "- Error function verbosity\n",
    "- Linear solver type\n",
    "\n",
    "## Usage Considerations\n",
    "\n",
    "- **Initial Guess**: The quality of the initial guess can significantly affect the convergence and performance of the Gauss-Newton optimizer.\n",
    "- **Non-convexity**: Since the method relies on linear approximations, it may struggle with highly non-convex problems or those with poor initial estimates.\n",
    "- **Performance**: The Gauss-Newton method is generally faster than other nonlinear optimization methods like Levenberg-Marquardt for problems that are well-approximated by a quadratic model near the solution.\n",
    "\n",
    "## Files\n",
    "\n",
    "- [GaussNewtonOptimizer.h](https://github.com/borglab/gtsam/blob/develop/gtsam/nonlinear/GaussNewtonOptimizer.h)\n",
    "- [GaussNewtonOptimizer.cpp](https://github.com/borglab/gtsam/blob/develop/gtsam/nonlinear/GaussNewtonOptimizer.cpp)"
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